One of the early explanation methods, found in [1]}, avoid the drawbacks mentioned above. In this explanation method, the weight of an attribute, on a particular prediction, is estimated by the difference of influence over the model with and without this attribute. This absence of an attribute is simulated by a weighted mean of the predictions of the model with all the possible values of the attribute, weighted by their probability of appearing in the dataset. This is faster than the method of [2]} as only one value is randomized. Later, in [3]}, the possibility of retraining the model entirely without the considered attribute in the dataset is proposed, which consists of an interesting trade-off: the time needed for retraining a model for each attribute of a dataset can be considerable, but once this training has been done, the prediction comparison can become near-instantaneous. These methods are named SHapley Additive exPlanations by Lundberg et al. [4]}, who regrouped a large number of similar methods of explanation, and detailed in Section .
